计算R中连接字符串中的统计信息_R_Dplyr_Stringr

计算R中连接字符串中的统计信息

计算R中连接字符串中的统计信息,r,dplyr,stringr,R,Dplyr,Stringr,假设我有这样一个数据帧： X. Name Type Total HP Attack Defense Sp..Atk Sp..Def Speed 795 718 Zygarde50% Forme Dragon/Ground 600 108 100 121 81 95 95 796 719 Diancie R

假设我有这样一个数据帧：

     X.                      Name             Type Total  HP Attack Defense Sp..Atk Sp..Def Speed
795 718          Zygarde50% Forme    Dragon/Ground   600 108    100     121      81      95    95
796 719                   Diancie       Rock/Fairy   600  50    100     150     100     150    50
797 719       DiancieMega Diancie       Rock/Fairy   700  50    160     110     160     110   110
798 720       HoopaHoopa Confined    Psychic/Ghost   600  80    110      60     150     130    70
799 720        HoopaHoopa Unbound     Psychic/Dark   680  80    160      60     170     130    80
800 721                 Volcanion       Fire/Water   600  80    110     120     130      90    70

如果我想计算每种类型的龙、地、石、仙等的平均属性（总数、生命、攻击、防御等）。。。（而不是输入龙/地，岩石/仙女），我将如何继续？属于任意两种类型的口袋妖怪的统计数据将用于计算这两种类型的平均统计数据

我已经使用

dplyr

包中的函数编写了代码：

summaryStats_byType<- summarise(byType,
                            count = n(),
                            averageTotal = mean(Total, na.rm = T),
                            averageHP = mean(HP, na.rm = T),
                            averageDefense = mean(Defense, na.rm = T),
                            averageSpAtk = mean(Sp..Atk, na.rm = T),
                            averageSpDef = mean(Sp..Def, na.rm = T),
                            averageSpeed = mean(Speed, na.rm = T))

summaryStats\u byType一种方法是以长格式拆分Type
列（我从splitstackshape
中选择了cSplit
来执行此操作），并像往常一样按分组，即
library(splitstackshape)
library(dplyr)

df1 <- cSplit(df, 'Type', sep = '/', 'long')
df1 %>% 
  group_by(Type) %>% 
  summarise_each(funs(mean), -c(X., Name))

# A tibble: 9 × 8
#     Type Total    HP Attack Defense Sp..Atk Sp..Def Speed
#   <fctr> <dbl> <dbl>  <dbl>   <dbl>   <dbl>   <dbl> <dbl>
#1    Dark   680    80    160      60     170     130    80
#2  Dragon   600   108    100     121      81      95    95
#3   Fairy   650    50    130     130     130     130    80
#4    Fire   600    80    110     120     130      90    70
#5   Ghost   600    80    110      60     150     130    70
#6  Ground   600   108    100     121      81      95    95
#7 Psychic   640    80    135      60     160     130    75
#8    Rock   650    50    130     130     130     130    80
#9   Water   600    80    110     120     130      90    70

这当然会产生同样的结果
数据
dput(df)
structure(list(X. = c(718L, 719L, 719L, 720L, 720L, 721L), Name = structure(c(6L, 
1L, 2L, 3L, 4L, 5L), .Label = c("Diancie", "DiancieMega_Diancie", 
"HoopaHoopa_Confined", "HoopaHoopa_Unbound", "Volcanion", "Zygarde50%_Forme"
), class = "factor"), Type = structure(c(1L, 5L, 5L, 4L, 3L, 
2L), .Label = c("Dragon/Ground", "Fire/Water", "Psychic/Dark", 
"Psychic/Ghost", "Rock/Fairy"), class = "factor"), Total = c(600L, 
600L, 700L, 600L, 680L, 600L), HP = c(108L, 50L, 50L, 80L, 80L, 
80L), Attack = c(100L, 100L, 160L, 110L, 160L, 110L), Defense = c(121L, 
150L, 110L, 60L, 60L, 120L), Sp..Atk = c(81L, 100L, 160L, 150L, 
170L, 130L), Sp..Def = c(95L, 150L, 110L, 130L, 130L, 90L), Speed = c(95L, 
50L, 110L, 70L, 80L, 70L)), .Names = c("X.", "Name", "Type", 
"Total", "HP", "Attack", "Defense", "Sp..Atk", "Sp..Def", "Speed"
), class = "data.frame", row.names = c("795", "796", "797", "798", 
"799", "800"))

一种方法是以长格式拆分Type
列（我从splitstackshape
中选择cSplit
来执行此操作），并像往常一样按分组，即
library(splitstackshape)
library(dplyr)

df1 <- cSplit(df, 'Type', sep = '/', 'long')
df1 %>% 
  group_by(Type) %>% 
  summarise_each(funs(mean), -c(X., Name))

# A tibble: 9 × 8
#     Type Total    HP Attack Defense Sp..Atk Sp..Def Speed
#   <fctr> <dbl> <dbl>  <dbl>   <dbl>   <dbl>   <dbl> <dbl>
#1    Dark   680    80    160      60     170     130    80
#2  Dragon   600   108    100     121      81      95    95
#3   Fairy   650    50    130     130     130     130    80
#4    Fire   600    80    110     120     130      90    70
#5   Ghost   600    80    110      60     150     130    70
#6  Ground   600   108    100     121      81      95    95
#7 Psychic   640    80    135      60     160     130    75
#8    Rock   650    50    130     130     130     130    80
#9   Water   600    80    110     120     130      90    70

这当然会产生同样的结果
数据
dput(df)
structure(list(X. = c(718L, 719L, 719L, 720L, 720L, 721L), Name = structure(c(6L, 
1L, 2L, 3L, 4L, 5L), .Label = c("Diancie", "DiancieMega_Diancie", 
"HoopaHoopa_Confined", "HoopaHoopa_Unbound", "Volcanion", "Zygarde50%_Forme"
), class = "factor"), Type = structure(c(1L, 5L, 5L, 4L, 3L, 
2L), .Label = c("Dragon/Ground", "Fire/Water", "Psychic/Dark", 
"Psychic/Ghost", "Rock/Fairy"), class = "factor"), Total = c(600L, 
600L, 700L, 600L, 680L, 600L), HP = c(108L, 50L, 50L, 80L, 80L, 
80L), Attack = c(100L, 100L, 160L, 110L, 160L, 110L), Defense = c(121L, 
150L, 110L, 60L, 60L, 120L), Sp..Atk = c(81L, 100L, 160L, 150L, 
170L, 130L), Sp..Def = c(95L, 150L, 110L, 130L, 130L, 90L), Speed = c(95L, 
50L, 110L, 70L, 80L, 70L)), .Names = c("X.", "Name", "Type", 
"Total", "HP", "Attack", "Defense", "Sp..Atk", "Sp..Def", "Speed"
), class = "data.frame", row.names = c("795", "796", "797", "798", 
"799", "800"))

您能dput
初始数据帧吗？（只需将dput（yourdataframe）
的输出添加到您的帖子中即可）。很抱歉，您能解释一下在这种情况下dput
的用法吗？“我真的不明白。”卡蒂特隆看我答案的最后一部分dput
生成数据帧的可复制示例您可以dput
初始数据帧吗？（只需将dput（yourdataframe）
的输出添加到您的帖子中即可）。很抱歉，您能解释一下在这种情况下dput
的用法吗？“我真的不明白。”卡蒂特隆看我答案的最后一部分dput
生成一个可复制的数据帧示例我不知道，我正在寻找另一种拆分方法！我不知道这一点，我正在寻找另一种分裂的方式！